一、前言
人工智能时代的到来，相信大家已耳濡目染，虹软免费，离线开放的人脸识别 SDK，正推动着全行业进入刷脸时代。为了方便开发者接入，虹软提供了多种语言，多种平台的人脸识别SDK的支持，使用场景广泛。产品主要功能有：人脸检测、追踪、特征提取、特征比对、属性检测，活体检测，图像质量检测等。此外，虹软提供的是基于本地算法特征的离线识别SDK，提供全平台的离线支持。
作为一名刚接触人脸识别的初学者，对于虹软极为简洁，方便的SDK接入充满了好奇，想试图应用到web领域，而如今Java最火的web框架非SpringBoot莫属。但对于Java语言，虹软官网暂时还没有提供基于SpringBoot的集成Demo，因此便尝试写个将Java的人脸识别SDK和SpringBoot进行集成的样例，并写此文章进行记录，向广大初学开发者作分享。
此Demo采用Maven作为项目管理工具，并基于Windows x64，Java 8 以及 SpringBoot 2.1.6，SDK是基于虹软人脸识别 SDK3.0。
二、项目结构
SDK依赖Jar包 可从虹软官网获取 点击”免费获取” ， ”登录“后 选择 具体“平台/版本/语言“ 获取。
三、项目依赖
pom.xml 依赖包括
SpringBoot-Web依赖SpringBoot-Devtools热部署依赖SpringBoot-Freemarker依赖，Hutool，Fastjson， Lombok，Commons-pool2，Guava虹软人脸识别SDK依赖Jar包SpringBoot-Maven插件
<dependency><groupId>org.projectlombok</groupId><artifactId>lombok</artifactId><version>1.18.8</version></dependency><dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-web</artifactId></dependency><dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-devtools</artifactId><optional>true</optional></dependency><dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-freemarker</artifactId></dependency><dependency><groupId>cn.hutool</groupId><artifactId>hutool-all</artifactId><version>4.6.1</version></dependency><dependency><groupId>com.alibaba</groupId><artifactId>fastjson</artifactId><version>1.2.59</version></dependency><dependency><groupId>mons</groupId><artifactId>commons-pool2</artifactId><version>2.6.0</version></dependency><dependency><groupId>com.google.guava</groupId><artifactId>guava</artifactId><version>26.0-jre</version></dependency><dependency><groupId>com.arcsoft.face</groupId><artifactId>arcsoft-sdk-face</artifactId><version>3.0.0.0</version><scope>system</scope><systemPath>${basedir}/lib/arcsoft-sdk-face-3.0.0.0.jar</systemPath></dependency><build><plugins><plugin><groupId>org.springframework.boot</groupId><artifactId>spring-boot-maven-plugin</artifactId><configuration><includeSystemScope>true</includeSystemScope><fork>true</fork></configuration></plugin></plugins></build>
四、项目流程
五、效果展示
在Application启动类 右击 选择 Run Application 即可运行程序，待程序启动完成后，访问 http://127.0.0.1:8089/
六、核心代码说明
1. application.properties 配置说明
#上传文件 最大值限制spring.servlet.multipart.max-file-size=100MB#请求 最大值限制spring.servlet.multipart.max-request-size=100MB#请求头 最大值限制server.max-http-header-size=2MB#请求体 最大值限制server.tomcat.max-http-post-size=50MB#项目访问端口server.port=8089#人脸识别引擎库路径config.arcface-sdk.sdk-lib-path=d:/arcsoft_lib#sdk appIdconfig.arcface-sdk.app-id=9iSfMeAhj********************Kes2TpSrd#sdk sdkKeyconfig.arcface-sdk.sdk-key=BuRTH3hGs9*******************yP9xu6fiFG7G#人脸识别 引擎池大小config.arcface-sdk.detect-pool-size=5#人脸比对 引擎池大小config.arcface-pare-pool-size=5#关闭freemarker模板引擎缓存spring.freemarker.cache=false#模板引擎更新延迟设置为0spring.freemarker.settings.template_update_delay=0
其中人脸识别引擎库，APP_ID，SDK_KEY可通过虹软官网”开发者中心“，进行 “登录”后 在“我的应用“中进行获取。
2. 项目实体类说明
1）UserRamCache 人脸信息存储类
public class UserRamCache {private static ConcurrentHashMap<String, UserInfo> userInfoMap = new ConcurrentHashMap<>();public static void addUser(UserInfo userInfo) {userInfoMap.put(userInfo.getFaceId(), userInfo);}public static void removeUser(String faceId) {userInfoMap.remove(faceId);}public static List<UserInfo> getUserList() {List<UserInfo> userInfoList = Lists.newLinkedList();for (UserInfo value : userInfoMap.values()) {userInfoList.add(value);}return userInfoList;}@Datapublic static class UserInfo {//人脸Idprivate String faceId;//人脸名称private String name;//人脸特征值private byte[] faceFeature;}}
此类拥有一个UserInfo的内部类，用于封装人脸信息，userInfoMap以人脸名称为key，UserInfo对象为Value 存储 并提供相应增/删/查功能的方法。
2）ProcessInfo 人脸检测实体类
public class ProcessInfo {//年龄private int age;//性别private int gender;//是否活体private int liveness;}
3）UserCompareInfo 人脸识别实体类 此类继承自 人脸信息存储类的人脸信息类(内部类)
public class UserCompareInfo extends UserRamCache.UserInfo {//人脸比对后的相似值private Float similar;}
4）FaceDetectResDTO 人脸检测DTO封装类
public class FaceDetectResDTO {//人脸框private Rect rect;//人脸角度private int orient;//人脸Idprivate int faceId = -1;//年龄private int age = -1;//性别private int gender = -1;//是否为活体private int liveness = -1;}
5）FaceRecognitionResDTO 人脸识别DTO封装类
public class FaceRecognitionResDTO {//人脸框private Rect rect;//人脸名称private String name;//人脸比对相似值private float similar;}
3. FaceEngineFactory类 源码说明
此类继承自BasePooledObjectFactory抽象类，为FaceEngine对象池。
1）成员变量说明
//SDK引擎库的路径private String libPath;//SDK APP_IDprivate String appId;//SDK SDK_KEYprivate String sdkKey;//SDK 激活码private String activeKey;//引擎配置类private EngineConfiguration engineConfiguration;
2）create()方法
@Overridepublic FaceEngine create() {FaceEngine faceEngine = new FaceEngine(libPath);int activeCode = faceEngine.activeOnline(appId, sdkKey);if (activeCode != ErrorInfo.MOK.getValue() && activeCode != ErrorInfo.MERR_ASF_ALREADY_ACTIVATED.getValue()) {log.error("引擎激活失败" + activeCode);throw new BusinessException(ErrorCodeEnum.FAIL, "引擎激活失败" + activeCode);}int initCode = faceEngine.init(engineConfiguration);if (initCode != ErrorInfo.MOK.getValue()) {log.error("引擎初始化失败" + initCode);throw new BusinessException(ErrorCodeEnum.FAIL, "引擎初始化失败" + initCode);}return faceEngine;}
参数说明：无返回结果：FaceEngine人脸识别引擎代码流程解读：
此方法，通过libPath(SDK引擎库的路径)实例化FaceEngine对象，再根据APP_ID，SDK_KEY调用activeOnline()方法激活引擎 (联网状态下)
成功激活引擎后，根据EngineConfiguration引擎配置类 调用init()方法初始化引擎 。
3）wrap()方法
public PooledObject<FaceEngine> wrap(FaceEngine faceEngine) {return new DefaultPooledObject<>(faceEngine);}
参数说明：FaceEngine人脸识别引擎返回结果：PooledObject包装类代码流程解读：
此方法，通过PooledObject包装器对象 将faceEngine进行包装，便于维护引擎的状态。
4）destroyObject()方法
public void destroyObject(PooledObject<FaceEngine> p) throws Exception {FaceEngine faceEngine = p.getObject();int result = faceEngine.unInit();super.destroyObject(p);}
参数说明：PooledObject包装类返回结果：无代码流程解读：
此方法，从PooledObject包装器对象中获取faceEngine引擎，随后卸载引擎。
4. FaceEngineServiceImpl类 源码说明
1）成员变量说明
@Value("${config.arcface-sdk.sdk-lib-path}")public String sdkLibPath;@Value("${config.arcface-sdk.app-id}")public String appId;@Value("${config.arcface-sdk.sdk-key}")public String sdkKey;@Value("${config.arcface-sdk.detect-pool-size}")public Integer detectPooSize;@Value("${config.arcface-pare-pool-size}")public Integer comparePooSize;private ExecutorService compareExecutorService;//通用人脸识别引擎池private GenericObjectPool<FaceEngine> faceEngineGeneralPool;//人脸比对引擎池private GenericObjectPool<FaceEngine> faceEngineComparePool;
此类的成员变量可通过@Value()注解获取配置文件中的相应配置。
2）init()方法
@PostConstructpublic void init() {GenericObjectPoolConfig detectPoolConfig = new GenericObjectPoolConfig();detectPoolConfig.setMaxIdle(detectPooSize);detectPoolConfig.setMaxTotal(detectPooSize);detectPoolConfig.setMinIdle(detectPooSize);detectPoolConfig.setLifo(false);EngineConfiguration detectCfg = new EngineConfiguration();FunctionConfiguration detectFunctionCfg = new FunctionConfiguration();//开启人脸检测功能detectFunctionCfg.setSupportFaceDetect(true);//开启人脸识别功能detectFunctionCfg.setSupportFaceRecognition(true);//开启年龄检测功能detectFunctionCfg.setSupportAge(true);//开启性别检测功能detectFunctionCfg.setSupportGender(true);//开启活体检测功能detectFunctionCfg.setSupportLiveness(true);detectCfg.setFunctionConfiguration(detectFunctionCfg);//图片检测模式，如果是连续帧的视频流图片，那么改成VIDEO模式detectCfg.setDetectMode(DetectMode.ASF_DETECT_MODE_IMAGE);//人脸旋转角度detectCfg.setDetectFaceOrientPriority(DetectOrient.ASF_OP_0_ONLY);//底层库算法对象池faceEngineGeneralPool = new GenericObjectPool(new FaceEngineFactory(sdkLibPath, appId, sdkKey, null, detectCfg), detectPoolConfig);//初始化特征比较线程池GenericObjectPoolConfig comparePoolConfig = new GenericObjectPoolConfig();comparePoolConfig.setMaxIdle(comparePooSize);comparePoolConfig.setMaxTotal(comparePooSize);comparePoolConfig.setMinIdle(comparePooSize);comparePoolConfig.setLifo(false);EngineConfiguration compareCfg = new EngineConfiguration();FunctionConfiguration compareFunctionCfg = new FunctionConfiguration();//开启人脸识别功能compareFunctionCfg.setSupportFaceRecognition(true);compareCfg.setFunctionConfiguration(compareFunctionCfg);//图片检测模式，如果是连续帧的视频流图片，那么改成VIDEO模式compareCfg.setDetectMode(DetectMode.ASF_DETECT_MODE_IMAGE);//人脸旋转角度compareCfg.setDetectFaceOrientPriority(DetectOrient.ASF_OP_0_ONLY);//底层库算法对象池faceEngineComparePool = new GenericObjectPool(new FaceEngineFactory(sdkLibPath, appId, sdkKey, null, compareCfg), comparePoolConfig);compareExecutorService = Executors.newFixedThreadPool(comparePooSize);}
参数说明：无返回结果：无代码流程解读：
@PostConstruct注解：Spring在实例化该Bean之后 立刻去执行此方法。在此方法中，首先去实例化通用人脸识别引擎池配置对象并设置其对应属性，之后实例化EngineConfiguration(设置图像检测模式、人脸旋转角度)和FunctionConfiguration(用于功能配置，开启引擎相应功能，被EngineConfiguration所依赖)，最后调FaceEngineFactory的构造方法去初始化引擎并获取对象池。人脸比对引擎池同理。
3）detectFaces()方法 人脸检测
@Overridepublic List<FaceInfo> detectFaces(ImageInfo imageInfo) {FaceEngine faceEngine = null;try {faceEngine = faceEngineGeneralPool.borrowObject();if (faceEngine == null) {throw new BusinessException(ErrorCodeEnum.FAIL, "获取引擎失败");}//人脸检测得到人脸列表List<FaceInfo> faceInfoList = new ArrayList<FaceInfo>();//人脸检测int errorCode = faceEngine.detectFaces(imageInfo.getImageData(), imageInfo.getWidth(), imageInfo.getHeight(), imageInfo.getImageFormat(), faceInfoList);if (errorCode == 0) {return faceInfoList;} else {log.error("人脸检测失败，errorCode：" + errorCode);} } catch (Exception e) {log.error("", e);} finally {if (faceEngine != null) {//释放引擎对象faceEngineGeneralPool.returnObject(faceEngine);}} return null;}
参数说明：ImageInfo图像信息返回结果：List<FaceInfo>人脸信息列表代码流程解读：
此方法，根据传入的ImageInfo图像信息，通过faceEngine引擎调用detectFaces()方法检测人脸信息(所需参数： 图像数据，图像宽度(4的倍数)，图片高度，图像的颜色格式，存放检测到的人脸信息List)，随后回收引擎对象。
注：detectFaces该功能依赖初始化的模式选择，初始化中detectFaceOrientPriority、detectFaceScaleVal、detectFaceMaxNum参数的设置，对能否检测到人脸以及检测到几张人脸都有决定性的作用。
4）extractFaceFeature()方法 人脸特征值提取
@Overridepublic byte[] extractFaceFeature(ImageInfo imageInfo, FaceInfo faceInfo) {FaceEngine faceEngine = null;try {faceEngine = faceEngineGeneralPool.borrowObject();if (faceEngine == null) {throw new BusinessException(ErrorCodeEnum.FAIL, "获取引擎失败");}FaceFeature faceFeature = new FaceFeature();//提取人脸特征int errorCode = faceEngine.extractFaceFeature(imageInfo.getImageData(), imageInfo.getWidth(), imageInfo.getHeight(), imageInfo.getImageFormat(), faceInfo, faceFeature);if (errorCode == 0) {return faceFeature.getFeatureData();} else {log.error("特征提取失败，errorCode：" + errorCode);} } catch (Exception e) {log.error("", e);} finally {if (faceEngine != null) {//释放引擎对象faceEngineGeneralPool.returnObject(faceEngine);}} return null;}
参数说明：ImageInfo图像信息，FaceInfo人脸信息返回结果：人脸特征值 字节数组代码流程解读：
此方法，根据传入的ImageInfo图像信息数据和FaceInfo人脸信息 通过faceEngine引擎调用extractFaceFeature()方法获取人脸特征数据(所需参数：图像数据，图像宽度(4的倍数)，图像高度，图像的颜色格式，人脸信息，存放提取到的人脸特征信息)，随后回收引擎对象。
注：extractFaceFeature()方法依赖detectFaces成功检测到人脸，将检测到的人脸，取单张人脸信息和使用的图像信息 传入该接口进行特征提取。
5）compareFace()方法 人脸相似度比对
@Overridepublic Float compareFace(ImageInfo imageInfo1, ImageInfo imageInfo2) {List<FaceInfo> faceInfoList1 = detectFaces(imageInfo1);List<FaceInfo> faceInfoList2 = detectFaces(imageInfo2);if (CollectionUtil.isEmpty(faceInfoList1) || CollectionUtil.isEmpty(faceInfoList2)) {throw new BusinessException(ErrorCodeEnum.FAIL,"未检测到人脸");}byte[] feature1 = extractFaceFeature(imageInfo1, faceInfoList1.get(0));byte[] feature2 = extractFaceFeature(imageInfo2, faceInfoList2.get(0));FaceEngine faceEngine = null;try {faceEngine = faceEngineGeneralPool.borrowObject();if (faceEngine == null) {throw new BusinessException(ErrorCodeEnum.FAIL, "获取引擎失败");}FaceFeature faceFeature1 = new FaceFeature();faceFeature1.setFeatureData(feature1);FaceFeature faceFeature2 = new FaceFeature();faceFeature2.setFeatureData(feature2);//提取人脸特征FaceSimilar faceSimilar = new FaceSimilar();int errorCode = pareFaceFeature(faceFeature1, faceFeature2, faceSimilar);if (errorCode == 0) {return faceSimilar.getScore();} else {log.error("特征提取失败，errorCode：" + errorCode);}} catch (Exception e) {log.error("", e);} finally {if (faceEngine != null) {//释放引擎对象faceEngineGeneralPool.returnObject(faceEngine);}} return null;}
参数说明：需要比对的两个ImageInfo图像信息返回结果：人脸比对相似值代码流程解读：
此方法，根据传入的两个ImageInfo图像信息分别调用detectFaces()方法获取各自人脸信息，成功检测到人脸信息后，再调用extractFaceFeature()方法提取各自人脸特征值，成功获取到人脸特征值后，根据两个特征值再通过faceEngine引擎调用compareFaceFeature()方法进行比对(所需参数：人脸特征值1，人脸特征值2，比对模型，存放比对相似值结果)， 最后获取人脸相似值返回 并回收引擎对象。
6）CompareFaceTask
FaceEngineServiceImpl的一个成员内部类，其实现Callable接口，用于完成线程任务
private class CompareFaceTask implements Callable<List<UserCompareInfo>> {//存储的人脸信息列表private List<UserRamCache.UserInfo> userInfoList;//目标特征值private FaceFeature targetFaceFeature;//相似度预期值private float passRate;public CompareFaceTask(List<UserRamCache.UserInfo> userInfoList, FaceFeature targetFaceFeature, float passRate) {this.userInfoList = userInfoList;this.targetFaceFeature = targetFaceFeature;this.passRate = passRate;}@Overridepublic List<UserCompareInfo> call() throws Exception {FaceEngine faceEngine = null;List<UserCompareInfo> resultUserInfoList = Lists.newLinkedList();//识别到的人脸列表try {faceEngine = faceEngineComparePool.borrowObject();for (UserRamCache.UserInfo userInfo : userInfoList) {FaceFeature sourceFaceFeature = new FaceFeature();sourceFaceFeature.setFeatureData(userInfo.getFaceFeature());FaceSimilar faceSimilar = new FaceSimilar();pareFaceFeature(targetFaceFeature, sourceFaceFeature, faceSimilar);if (faceSimilar.getScore() > passRate) {//相似值大于配置预期，加入到识别到人脸的列表UserCompareInfo info = new UserCompareInfo();info.setName(userInfo.getName());info.setFaceId(userInfo.getFaceId());info.setSimilar(faceSimilar.getScore());resultUserInfoList.add(info);}}} catch (Exception e) {logger.error("", e);} finally {if (faceEngine != null) {faceEngineComparePool.returnObject(faceEngine);}}return resultUserInfoList;}}
参数说明：无返回结果：List<UserCompareInfo>人脸识别实体类列表代码流程解读：
call()方法中遍历userInfoList中每个UserInfo获取特征值，并结合目标特征值 通过faceEngine引擎调用compareFaceFeature()方法获取相似度大小 ,将获取到的相似度大小和预期相似度进行比较，若大于配置的预期值，则加入到识别到人脸的列表，最后回收引擎对象。
7）faceRecognition()方法 人脸识别
@Overridepublic List<UserCompareInfo> faceRecognition(byte[] faceFeature, List<UserRamCache.UserInfo> userInfoList, float passRate) {List<UserCompareInfo> resultUserInfoList = Lists.newLinkedList();//识别到的人脸列表FaceFeature targetFaceFeature = new FaceFeature();targetFaceFeature.setFeatureData(faceFeature);List<List<UserRamCache.UserInfo>> faceUserInfoPartList = Lists.partition(userInfoList, 1000);//分成1000一组，多线程处理CompletionService<List<UserCompareInfo>> completionService = new ExecutorCompletionService(compareExecutorService);for (List<UserRamCache.UserInfo> part : faceUserInfoPartList) {completionService.submit(new CompareFaceTask(part, targetFaceFeature, passRate));}for (int i = 0; i < faceUserInfoPartList.size(); i++) {List<UserCompareInfo> faceUserInfoList = null;try {faceUserInfoList = completionService.take().get();} catch (InterruptedException | ExecutionException e) {}if (CollectionUtil.isNotEmpty(userInfoList)) {resultUserInfoList.addAll(faceUserInfoList);}}resultUserInfoList.sort((h1, h2) -> h2.getSimilar().compareTo(h1.getSimilar()));//从大到小排序return resultUserInfoList;}
参数说明：人脸特征值 字节数组，List<UserRamCache.UserInfo>存储的人脸信息列表，相似度预期值返回结果：List<UserCompareInfo>人脸识别实体类列表代码流程解读：
此方法，根据传入的人脸特征值以及UserRamCache.UserInfo列表，先将UserRamCache.UserInfo分为每1000一组，再通过多线程处理，即上述CompareFaceTask类，处理完之后再将结果合并，按从大到小排序后返回。
8）process()方法 人脸属性检测
@Overridepublic List<ProcessInfo> process(ImageInfo imageInfo, List<FaceInfo> faceInfoList) {FaceEngine faceEngine = null;try {//获取引擎对象faceEngine = faceEngineGeneralPool.borrowObject();if (faceEngine == null) {throw new BusinessException(ErrorCodeEnum.FAIL, "获取引擎失败");}int errorCode = faceEngine.process(imageInfo.getImageData(), imageInfo.getWidth(), imageInfo.getHeight(), imageInfo.getImageFormat(), faceInfoList, FunctionConfiguration.builder().supportAge(true).supportGender(true).supportLiveness(true).build());if (errorCode == 0) {List<ProcessInfo> processInfoList = Lists.newLinkedList();//性别列表List<GenderInfo> genderInfoList = new ArrayList<GenderInfo>();faceEngine.getGender(genderInfoList);//年龄列表List<AgeInfo> ageInfoList = new ArrayList<AgeInfo>();faceEngine.getAge(ageInfoList);//活体结果列表List<LivenessInfo> livenessInfoList = new ArrayList<LivenessInfo>();faceEngine.getLiveness(livenessInfoList);for (int i = 0; i < genderInfoList.size(); i++) {ProcessInfo processInfo = new ProcessInfo();processInfo.setGender(genderInfoList.get(i).getGender());processInfo.setAge(ageInfoList.get(i).getAge());processInfo.setLiveness(livenessInfoList.get(i).getLiveness());processInfoList.add(processInfo);}return processInfoList;}} catch (Exception e) {logger.error("", e);} finally {if (faceEngine != null) {//释放引擎对象faceEngineGeneralPool.returnObject(faceEngine);}}return null;}
参数说明：ImageInfo图像信息，List<FaceInfo>人脸信息列表返回结果：List<ProcessInfo>人脸检测实体类列表代码流程解读：
此方法，根据传入的ImageInfo图像信息以及检测到的FaceInfo人脸信息列表，通过faceEngine引擎调用process()方法(所需参数：图像数据，图片宽度(4的倍数),图像高度，图像的颜色空间格式，人脸信息列表，需检测的属性)，之后可从faceEngine对象获取 性别，年龄，是否活体等结果的列表，并将一系列列表结果遍历 设置于ProcessInfo对象返回，随后回收引擎对象。
注：process()支持检测AGE、GENDER、FACE3DANGLE、LIVENESS四种属性，若想检测这些属性，须在初始化引擎接口中对想要检测的属性进行设置。
5. FaceController类 源码说明
1）initFace()方法：初始化注册人脸
@PostConstructpublic void initFace() throws FileNotFoundException {Map<String, String> fileMap = Maps.newHashMap();fileMap.put("zhao1", "赵丽颖");fileMap.put("yang1", "杨紫");for (String f : fileMap.keySet()) {ClassPathResource resource = new ClassPathResource("static/images/" + f + ".jpg");InputStream inputStream = null;try {inputStream = resource.getInputStream();} catch (IOException e) {}ImageInfo rgbData = ImageFactory.getRGBData(inputStream);List<FaceInfo> faceInfoList = faceEngineService.detectFaces(rgbData);if (CollectionUtil.isNotEmpty(faceInfoList)) {byte[] feature = faceEngineService.extractFaceFeature(rgbData, faceInfoList.get(0));UserRamCache.UserInfo userInfo = new UserCompareInfo();userInfo.setFaceId(f);userInfo.setName(fileMap.get(f));userInfo.setFaceFeature(feature);UserRamCache.addUser(userInfo);}}log.info("http://127.0.0.1:"+ port +"/");}
参数说明：无返回结果：无代码流程解读：
@PostConstruct注解表示Spring在实例化该Bean之后 立刻去执行此方法。首先去加载static/images/下的图片资源将其解析为ImageInfo类型的RGB图像信息数据，之后依次调用FaceEngineService类的detectFaces()，extractFaceFeature()方法提取人脸特征值，最后将人脸相关数据设置于UserRamCache.UserInfo对象中(此Demo仅将数据暂存于内存中，用户可根据需要，自行创建数据库相关表并持久化于磁盘中)。
2）faceAdd()方法 添加人脸
@RequestMapping(value = "/faceAdd", method = RequestMethod.POST)@ResponseBodypublic Response faceAdd(String file, String faceId, String name) {return null;}
参数说明：浏览器上传的图片信息，人脸Id，人脸名返回结果：Json格式代码流程解读：
此方法，可用于添加更多人脸信息，根据用户需要自行完善。
3）faceRecognition()方法 人脸识别
@RequestMapping(value = "/faceRecognition", method = RequestMethod.POST)@ResponseBodypublic Response<List<FaceRecognitionResDTO>> faceRecognition(String image) {List<FaceRecognitionResDTO> faceRecognitionResDTOList = Lists.newLinkedList();byte[] bytes = Base64Util.base64ToBytes(image);ImageInfo rgbData = ImageFactory.getRGBData(bytes);List<FaceInfo> faceInfoList = faceEngineService.detectFaces(rgbData);if (CollectionUtil.isNotEmpty(faceInfoList)) {for (FaceInfo faceInfo : faceInfoList) {FaceRecognitionResDTO faceRecognitionResDTO = new FaceRecognitionResDTO();faceRecognitionResDTO.setRect(faceInfo.getRect());byte[] feature = faceEngineService.extractFaceFeature(rgbData, faceInfo);if (feature != null) {List<UserCompareInfo> userCompareInfos = faceEngineService.faceRecognition(feature, UserRamCache.getUserList(), 0.8f);if (CollectionUtil.isNotEmpty(userCompareInfos)) {faceRecognitionResDTO.setName(userCompareInfos.get(0).getName());faceRecognitionResDTO.setSimilar(userCompareInfos.get(0).getSimilar());}}faceRecognitionResDTOList.add(faceResDTOList);}}return Response.newSuccessResponse(faceRecognitionResDTOList);}
参数说明：浏览器上传的图片信息返回结果：Json格式List<FaceRecognitionResDTO>人脸识别DTO列表代码流程解读：
此方法，先将请求上传的(base64编码后)的图片解析为ImageInfo类型的RGB图像信息数据，再依次调用FaceEngineService类的detectFaces()，extractFaceFeature()，faceRecognition()方法 与先前存于内存中的人脸信息进行比对，获取相似度最大的人脸信息，并将结果设置于FaceRecognitionResDTO后返回。
4）detectFaces() 方法 人脸检测
@RequestMapping(value = "/detectFaces", method = RequestMethod.POST)@ResponseBodypublic Response<List<FaceDetectResDTO>> detectFaces(String image) {byte[] bytes = Base64Util.base64ToBytes(image);ImageInfo rgbData = ImageFactory.getRGBData(bytes);List<FaceDetectResDTO> faceDetectResDTOS = Lists.newLinkedList();List<FaceInfo> faceInfoList = faceEngineService.detectFaces(rgbData);if (CollectionUtil.isNotEmpty(faceInfoList)) {List<ProcessInfo> process = faceEngineService.process(rgbData, faceInfoList);for (int i = 0; i < faceInfoList.size(); i++) {FaceDetectResDTO faceDetectResDTO = new FaceDetectResDTO();FaceInfo faceInfo = faceInfoList.get(i);faceDetectResDTO.setRect(faceInfo.getRect());faceDetectResDTO.setOrient(faceInfo.getOrient());faceDetectResDTO.setFaceId(faceInfo.getFaceId());if (CollectionUtil.isNotEmpty(process)) {ProcessInfo processInfo = process.get(i);faceDetectResDTO.setAge(processInfo.getAge());faceDetectResDTO.setGender(processInfo.getGender());faceDetectResDTO.setLiveness(processInfo.getLiveness());}faceDetectResDTOS.add(faceDetectResDTO);}}return Response.newSuccessResponse(faceDetectResDTOS);}
参数说明：浏览器上传的图片信息返回结果：Json格式List<FaceDetectResDTO>人脸检测DTO列表代码流程解读：
此方法，先将请求上传的(base64编码后)的图片解析为ImageInfo类型的RGB图像信息数据，再依次调用FaceEngineService类的detectFaces()process()方法获取人脸检测数据(年龄，性别，是否活体)，并将结果设置于FaceDetectResDTO后返回。
5）compareFaces()方法 人脸比对
@RequestMapping(value = "/compareFaces", method = RequestMethod.POST)@ResponseBodypublic Response<Float> compareFaces(String image1, String image2) {byte[] bytes1 = Base64Util.base64ToBytes(image1);byte[] bytes2 = Base64Util.base64ToBytes(image2);ImageInfo rgbData1 = ImageFactory.getRGBData(bytes1);ImageInfo rgbData2 = ImageFactory.getRGBData(bytes2);Float similar = pareFace(rgbData1, rgbData2);return Response.newSuccessResponse(smilar);}
参数说明：两张浏览器上传的图片信息返回结果：人脸比对相似值代码流程解读：
此方法，先将请求上传的(base64编码后)的图片解析为ImageInfo类型的RGB图像信息数据，之后通过FaceEngineService类的compareFace()方法进行人脸比对，获取人脸相似值并返回。
七、源码下载
若有想一起学习虹软SDK，感受人脸识别奥秘的同学，可通过点击此链接获取Demo源码。
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